Gaussian Process Regression´s Hyperparameters Optimization to Predict Financial Distress

Jakub Horak, Amine Sabek
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Abstract

Predicting financial distress has become one of the most important topics of the hour that has swept the accounting and financial field due to its significant correlation with the development of science and technology. The main objective of this paper is to predict financial distress based on the Gaussian Process Regression (GPR) and then compare the results of this model with the results of other deep learning models (SVM, LR, LD, DT, KNN). The analysis is based on a dataset of 352 companies extracted from the Kaggle database. As for predictors, 83 financial ratios were used. The study concluded that the use of GPR achieves very relevant results. Furthermore, it outperformed the rest of the deep learning models and achieved first place equally with the SVM model with a classification accuracy of 81%. The results contribute to the maintenance of the integrated system and the prosperity of the country’s economy, the prediction of the financial distress of companies and thus the potential prevention of disruption of the given system.
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高斯过程回归的超参数优化预测财务困境
财务困境预测由于与科学技术的发展有着显著的相关性,已成为席卷会计和金融领域的重要课题之一。本文的主要目的是基于高斯过程回归(GPR)预测财务困境,然后将该模型的结果与其他深度学习模型(SVM, LR, LD, DT, KNN)的结果进行比较。该分析基于从Kaggle数据库中提取的352家公司的数据集。至于预测指标,使用了83个财务比率。研究得出结论,使用探地雷达取得了非常相关的结果。此外,它优于其他深度学习模型,并以81%的分类准确率与SVM模型并列第一。其结果有助于维持综合系统和国家经济的繁荣,预测公司的财务困境,从而潜在地防止给定系统的中断。
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来源期刊
CiteScore
2.70
自引率
0.00%
发文量
21
审稿时长
12 weeks
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